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      Trends in Programming Languages for Neuroscience Simulations

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          Abstract

          Neuroscience simulators allow scientists to express models in terms of biological concepts, without having to concern themselves with low-level computational details of their implementation. The expressiveness, power and ease-of-use of the simulator interface is critical in efficiently and accurately translating ideas into a working simulation. We review long-term trends in the development of programmable simulator interfaces, and examine the benefits of moving from proprietary, domain-specific languages to modern dynamic general-purpose languages, in particular Python, which provide neuroscientists with an interactive and expressive simulation development environment and easy access to state-of-the-art general-purpose tools for scientific computing.

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          PyNN: A Common Interface for Neuronal Network Simulators

          Andrew P. Davison, Daniel Brüderle, Jochen Martin Eppler … (2009)
          Computational neuroscience has produced a diversity of software for simulations of networks of spiking neurons, with both negative and positive consequences. On the one hand, each simulator uses its own programming or configuration language, leading to considerable difficulty in porting models from one simulator to another. This impedes communication between investigators and makes it harder to reproduce and build on the work of others. On the other hand, simulation results can be cross-checked between different simulators, giving greater confidence in their correctness, and each simulator has different optimizations, so the most appropriate simulator can be chosen for a given modelling task. A common programming interface to multiple simulators would reduce or eliminate the problems of simulator diversity while retaining the benefits. PyNN is such an interface, making it possible to write a simulation script once, using the Python programming language, and run it without modification on any supported simulator (currently NEURON, NEST, PCSIM, Brian and the Heidelberg VLSI neuromorphic hardware). PyNN increases the productivity of neuronal network modelling by providing high-level abstraction, by promoting code sharing and reuse, and by providing a foundation for simulator-agnostic analysis, visualization and data-management tools. PyNN increases the reliability of modelling studies by making it much easier to check results on multiple simulators. PyNN is open-source software and is available from http://neuralensemble.org/PyNN.
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            Brian: A Simulator for Spiking Neural Networks in Python

            Dan FM Goodman, Romain Brette (2008)
            “Brian” is a new simulator for spiking neural networks, written in Python (http://brian. di.ens.fr). It is an intuitive and highly flexible tool for rapidly developing new models, especially networks of single-compartment neurons. In addition to using standard types of neuron models, users can define models by writing arbitrary differential equations in ordinary mathematical notation. Python scientific libraries can also be used for defining models and analysing data. Vectorisation techniques allow efficient simulations despite the overheads of an interpreted language. Brian will be especially valuable for working on non-standard neuron models not easily covered by existing software, and as an alternative to using Matlab or C for simulations. With its easy and intuitive syntax, Brian is also very well suited for teaching computational neuroscience.
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              NEURON and Python

              Michael L. Hines, Andrew P. Davison, Eilif Muller (2009)
              The NEURON simulation program now allows Python to be used, alone or in combination with NEURON's traditional Hoc interpreter. Adding Python to NEURON has the immediate benefit of making available a very extensive suite of analysis tools written for engineering and science. It also catalyzes NEURON software development by offering users a modern programming tool that is recognized for its flexibility and power to create and maintain complex programs. At the same time, nothing is lost because all existing models written in Hoc, including graphical user interface tools, continue to work without change and are also available within the Python context. An example of the benefits of Python availability is the use of the xml module in implementing NEURON's Import3D and CellBuild tools to read MorphML and NeuroML model specifications.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Front Neurosci
                Journal ID (publisher-id): Front. Neurosci.
                Title: Frontiers in Neuroscience
                Publisher: Frontiers Research Foundation
                ISSN (Print): 1662-4548
                ISSN (Electronic): 1662-453X
                Publication date (Electronic preprint): 04 September 2009
                Publication date (Electronic): 15 December 2009
                Publication date Collection: December 2009
                Volume: 3
                Issue: 3
                Pages: 374-380
                Affiliations
                [1] 1simpleUnité de Neurosciences Intégratives et Computationnelles, Centre National de la Recherche Scientifique Gif sur Yvette, France
                [2] 2simpleComputer Science, Yale University New Haven, CT, USA
                [3] 3simpleLaboratory for Computational Neuroscience, Ecole Polytechnique Fédérale de Lausanne Lausanne, Switzerland
                Author notes

                Edited by: Rolf Kötter, Radboud University Nijmegen, The Netherlands

                Reviewed by: Felix Schürmann, Ecole Polytechnique Fédérale de Lausanne, Switzerland; Marc-Oliver Gewaltig, Honda Research Institute Europe GmbH, Germany; Volker Steuber, University of Hertfordshire, UK

                Article
                DOI: 10.3389/neuro.01.036.2009
                PMC ID: 2796921
                PubMed ID: 20198154
                SO-VID: 0fdafcfd-ffdc-4958-8188-7098a1072efe
                Copyright © Copyright © 2009 Davison, Hines and Muller.
                License:

                This is an open-access article subject to an exclusive license agreement between the authors and the Frontiers Research Foundation, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are credited.

                History
                Date received : 31 July 2009
                Date accepted : 02 October 2009
                Page count
                Figures: 1, Tables: 0, Equations: 0, References: 31, Pages: 7, Words: 5160
                Categories
                Subject: Neuroscience
                Subject: Focused Review

                ScienceOpen disciplines: Neurosciences
                Keywords: simulation,computational neuroscience,python
                Data availability:
                ScienceOpen disciplines: Neurosciences
                Keywords: simulation, computational neuroscience, python

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